Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B-1 Concentration Classification in Single Maize Kernels
文献类型： 外文期刊
作者： Zhou, Quan 1 ; Huang, Wenqian 2 ; Tian, Xi 2 ;
作者机构： 1.West Anhui Univ, Sch Elect & Informat Engn, Luan 237000, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China
关键词： aflatoxin B-1; maize kernels; characteristic wavelengths; gray-value difference of image
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）
ISSN：
年卷期： 2022 年 12 卷 3 期
页码：
收录情况： SCI
摘要： Aflatoxin B-1 (AFB(1)) is a very strong carcinogen, maize kernels are easily infected by this toxin during storage. Rapid and accurate identification of AFB(1) is of great significance to ensure food safety. In this study, a novel method for classification of AFB(1) in single maize kernels was developed. Four groups of maize kernel samples with different AFB(1) concentrations (10, 20, 50, and 100 ppb) were prepared by artificial inoculation of toxin. In addition, one group of maize kernel samples without AFB(1) were prepared as control, each group with 70 samples. The visible and short wave near-infrared (Vis-SWNIR) region (500-1000 nm) and long wave near-infrared (LWNIR) region (1000-2000 nm) hyperspectral images of all samples were obtained respectively, and the hyperspectral images in 500-2000 nm range was obtained after spectral pretreatment and fusion. Kennard-Stone algorithm was used to divide the samples into calibration set or prediction set. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to roughly select the characteristic wavelengths of the calibration set samples, and 25 and 26 effective wavelengths were obtained respectively. Based on the roughly selected wavelengths, a method of fine selection of the characteristic wavelengths was proposed by using the gray-value difference of image (GDI), and a few number of characteristic wavelengths were further selected. Under the LDA classification model, 10 characteristic wavelengths were selected to test the prediction set and the independent verification samples, and the ideal result were obtained with an accuracy of 94.46% and 91.11%, respectively. This study provides a new approach for AFB(1) concentration classification of single maize kernels.
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